Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
This addresses governance problems for stakeholders in domains like finance and healthcare, but it is incremental as it builds on existing blockchain and agent technologies.
The paper tackles the governance and accountability challenges of LLM-empowered autonomous agents in multi-agent collaboration by proposing a blockchain-enabled layered architecture with modules for behavior tracing, reputation evaluation, and malicious behavior forecasting, establishing a foundation for trustworthy regulatory mechanisms.
Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities pose substantial governance and accountability challenges. In this paper, we propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer. Within this framework, we design three key modules: (i) an agent behavior tracing and arbitration module for automated accountability, (ii) a dynamic reputation evaluation module for trust assessment in collaborative scenarios, and (iii) a malicious behavior forecasting module for early detection of adversarial activities. Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems. Finally, we discuss the future research directions for blockchain-enabled regulatory frameworks in multi-agent systems.